Improving creep strength of the fine-grained heat-affected zone of novel 9Cr martensitic heat-resistant steel via modified thermo-mechanical treatment

The infamous type Ⅳ failure within the fine-grained heat-affected zone (FGHAZ) in G115 steel weldments seriously threatens the safe operation of ultra-supercritical (USC) power plants.In this work,the traditional thermo-mechanical treatment was modified via the replacement of hot-rolling with cold rolling,i.e.,normalizing,cold rolling,and tempering (NCT),which was developed to improve the creep strength of the FGHAZ in G115 steel weldments.The NCT treatment effectively promoted the dissolution of preformed M_(23)C_(6)particles and relieved the boundary segregation of C and Cr during welding thermal cycling,which accelerated the dispersed reprecipitation of M_(23)C_(6) particles within the fresh reaustenitized grains during post-weld heat treatment.In addition,the precipitation of Cu-rich phases and MX particles was promoted evidently due to the deformation-induced dislocations.As a result,the interacting actions between precipitates,dislocations,and boundaries during creep were reinforced considerably.Following this strategy,the creep rupture life of the FGHAZ in G115 steel weldments can be prolonged by 18.6%,which can further push the application of G115 steel in USC power plants.

Flume experiments to study fine-grain migration and its impact on slope stability

Fine grains migration is a primary cause of landslides and debris flows.This study investigates the effect of fine-grain migration on slope failure through flume experiments,focusing on the spatiotemporal characteristics and mechanisms of slope stability.A series of artificial rainfall flume experiments with varying rainfall intensities and slopes were conducted using soil samples collected from Wei Jia Gully.The experiments monitored pore-water pressure,grain migration,and failure sequences.Grain-size distribution parameters(μand Dc)were analyzed to understand the migration path and accumulation of fine grains.The experiments reveal that fine-grain migration significantly alters soil structure,leading to random blockage and interconnection of internal pore channels.These changes result in fluctuating pore-water pressure distributions and uneven fine-grain accumulation,critical factors in slope stability.Slope failures occur randomly and intermittently,influenced by fine-grain content in runoff and resulting pore-water pressure variations.This study highlights that fine-grain migration plays a vital role in slope stability,with significant implications for predicting and mitigating slope failures.The stochastic nature of fine-grain migration and its impact on soil properties should be incorporated into predictive models to enhance their accuracy and reliability.

Fine-Grained Ship Recognition Based on Visible and Near-Infrared Multimodal Remote Sensing Images: Dataset,Methodology and Evaluation

Fine-grained recognition of ships based on remote sensing images is crucial to safeguarding maritime rights and interests and maintaining national security.Currently,with the emergence of massive high-resolution multi-modality images,the use of multi-modality images for fine-grained recognition has become a promising technology.Fine-grained recognition of multi-modality images imposes higher requirements on the dataset samples.The key to the problem is how to extract and fuse the complementary features of multi-modality images to obtain more discriminative fusion features.The attention mechanism helps the model to pinpoint the key information in the image,resulting in a significant improvement in the model’s performance.In this paper,a dataset for fine-grained recognition of ships based on visible and near-infrared multi-modality remote sensing images has been proposed first,named Dataset for Multimodal Fine-grained Recognition of Ships(DMFGRS).It includes 1,635 pairs of visible and near-infrared remote sensing images divided into 20 categories,collated from digital orthophotos model provided by commercial remote sensing satellites.DMFGRS provides two types of annotation format files,as well as segmentation mask images corresponding to the ship targets.Then,a Multimodal Information Cross-Enhancement Network(MICE-Net)fusing features of visible and near-infrared remote sensing images,has been proposed.In the network,a dual-branch feature extraction and fusion module has been designed to obtain more expressive features.The Feature Cross Enhancement Module(FCEM)achieves the fusion enhancement of the two modal features by making the channel attention and spatial attention work cross-functionally on the feature map.A benchmark is established by evaluating state-of-the-art object recognition algorithms on DMFGRS.MICE-Net conducted experiments on DMFGRS,and the precision,recall,mAP0.5 and mAP0.5:0.95 reached 87%,77.1%,83.8%and 63.9%,respectively.Extensive experiments demonstrate that the proposed MICE-Net has more excellent performance on DMFGRS.Built on lightweight network YOLO,the model has excellent generalizability,and thus has good potential for application in real-life scenarios.

Fine-grained grid computing model for Wi-Fi indoor localization in complex environments

The fingerprinting-based approach using the wireless local area network(WLAN)is widely used for indoor localization.However,the construction of the fingerprint database is quite time-consuming.Especially when the position of the access point(AP)or wall changes,updating the fingerprint database in real-time is difficult.An appropriate indoor localization approach,which has a low implementation cost,excellent real-time performance,and high localization accuracy and fully considers complex indoor environment factors,is preferred in location-based services(LBSs)applications.In this paper,we proposed a fine-grained grid computing(FGGC)model to achieve decimeter-level localization accuracy.Reference points(RPs)are generated in the grid by the FGGC model.Then,the received signal strength(RSS)values at each RP are calculated with the attenuation factors,such as the frequency band,three-dimensional propagation distance,and walls in complex environments.As a result,the fingerprint database can be established automatically without manual measurement,and the efficiency and cost that the FGGC model takes for the fingerprint database are superior to previous methods.The proposed indoor localization approach,which estimates the position step by step from the approximate grid location to the fine-grained location,can achieve higher real-time performance and localization accuracy simultaneously.The mean error of the proposed model is 0.36 m,far lower than that of previous approaches.Thus,the proposed model is feasible to improve the efficiency and accuracy of Wi-Fi indoor localization.It also shows high-accuracy performance with a fast running speed even under a large-size grid.The results indicate that the proposed method can also be suitable for precise marketing,indoor navigation,and emergency rescue.

Integrating Ontology-Based Approaches with Deep Learning Models for Fine-Grained Sentiment Analysis

Although sentiment analysis is pivotal to understanding user preferences,existing models face significant challenges in handling context-dependent sentiments,sarcasm,and nuanced emotions.This study addresses these challenges by integrating ontology-based methods with deep learning models,thereby enhancing sentiment analysis accuracy in complex domains such as film reviews and restaurant feedback.The framework comprises explicit topic recognition,followed by implicit topic identification to mitigate topic interference in subsequent sentiment analysis.In the context of sentiment analysis,we develop an expanded sentiment lexicon based on domainspecific corpora by leveraging techniques such as word-frequency analysis and word embedding.Furthermore,we introduce a sentiment recognition method based on both ontology-derived sentiment features and sentiment lexicons.We evaluate the performance of our system using a dataset of 10,500 restaurant reviews,focusing on sentiment classification accuracy.The incorporation of specialized lexicons and ontology structures enables the framework to discern subtle sentiment variations and context-specific expressions,thereby improving the overall sentiment-analysis performance.Experimental results demonstrate that the integration of ontology-based methods and deep learning models significantly improves sentiment analysis accuracy.

Experimental Study on the Effect of Fine-Grained Soil Content on the Freezing Strength of Aeolian Sand-Cement Interface

In cold regions,understanding the freezing strength of the interface between soil and structure is crucial for designing frost-resistant foundations.To investigate how the content of cement powder in aeolian sand affects this strength,we conducted direct shear tests under various conditions such as different fine-grained soil content,normal stress,and initial moisture content of the soil.By analyzing parameters like soil properties,and volume of ice content,and using the Mohr-Coulomb strength theory to define interface strength,we aimed to indirectly measure the cementation strength of the interface.Our findings revealed that as the particle content increased,the interface stress-strain curves became noticeably stiffer.We also observed a positive linear relationship between freezing strength and silt content,while the initial moisture content of the soil did not significantly impact the strengthening effect of fine-grained soil on freezing strength.Moreover,we discovered that as the powder content increased,the force binding the ice to the interface decreased,while the friction angle at the interface increased.However,the cohesion force at the interface remained relatively unchanged.Overall,our analysis suggests that the increase in freezing strength due to fine-grained soil content is primarily due to the heightened friction between aeolian sand and the interface.

Fine-grained gravity flow sedimentation and its influence on development of shale oil sweet sections in lacustrine basins in China

The geological conditions and processes of fine-grained gravity flow sedimentation in continental lacustrine basins in China are analyzed to construct the model of fine-grained gravity flow sedimentation in lacustrine basin,reveal the development laws of fine-grained deposits and source-reservoir,and identify the sweet sections of shale oil.The results show that fine-grained gravity flow is one of the important sedimentary processes in deep lake environment,and it can transport fine-grained clasts and organic matter in shallow water to deep lake,forming sweet sections and high-quality source rocks of shale oil.Fine-grained gravity flow deposits in deep waters of lacustrine basins in China are mainly fine-grained high-density flow,fine-grained turbidity flow(including surge-like turbidity flow and fine-grained hyperpycnal flow),fine-grained viscous flow(including fine-grained debris flow and mud flow),and fine-grained transitional flow deposits.The distribution of fine-grained gravity flow deposits in the warm and humid unbalanced lacustrine basins are controlled by lake-level fluctuation,flooding events,and lakebed paleogeomorphology.During the lake-level rise,fine-grained hyperpycnal flow caused by flooding formed fine-grained channel–levee–lobe system in the flat area of the deep lake.During the lake-level fall,the sublacustrine fan system represented by unconfined channel was developed in the flexural slope breaks and sedimentary slopes of depressed lacustrine basins,and in the steep slopes of faulted lacustrine basins;the sublacustrine fan system with confined or unconfined channel was developed on the gentle slopes and in axial direction of faulted lacustrine basins,with fine-grained gravity flow deposits possibly existing in the lower fan.Within the fourth-order sequences,transgression might lead to organic-rich shale and fine-grained hyperpycnal flow deposits,while regression might cause fine-grained high-density flow,surge-like turbidity flow,fine-grained debris flow,mud flow,and fine-grained transitional flow deposits.Since the Permian,in the shale strata of lacustrine basins in China,multiple transgression-regression cycles of fourth-order sequences have formed multiple source-reservoir assemblages.Diverse fine-grained gravity flow sedimentation processes have created sweet sections of thin siltstone consisting of fine-grained high-density flow,fine-grained hyperpycnal flow and surge-like turbidity flow deposits,sweet sections with interbeds of mudstone and siltstone formed by fine-grained transitional flows,and sweet sections of shale containing silty and muddy clasts and with horizontal bedding formed by fine-grained debris flow and mud flow.The model of fine-grained gravity flow sedimentation in lacustrine basin is significant for the scientific evaluation of sweet shale oil reservoir and organic-rich source rock.

Prediction of compaction parameters for fine-grained soil: Critical comparison of the deep learning and standalone models

A comparison between deep learning and standalone models in predicting the compaction parameters of soil is presented in this research.One hundred and ninety and fifty-three soil samples were randomly picked up from two hundred and forty-three soil samples to create training and validation datasets,respectively.The performance and accuracy of the models were measured by root mean square error(RMSE),coefficient of determination(R2),Pearson product-moment correlation coefficient(r),mean absolute error(MAE),variance accounted for(VAF),mean absolute percentage error(MAPE),weighted mean absolute percentage error(WMAPE),a20-index,index of scatter(IOS),and index of agreement(IOA).Comparisons between standalone models demonstrate that the model MD 29 in Gaussian process regression(GPR)and model MD 101 in support vector machine(SVM)can achieve over 96%of accuracy in predicting the optimum moisture content(OMC)and maximum dry density(MDD)of soil,and outperformed other standalone models.The comparison between deep learning models shows that the models MD 46 and MD 146 in long short-term memory(LSTM)predict OMC and MDD with higher accuracy than ANN models.However,the LSTM models outperformed the GPR models in predicting the compaction parameters.The sensitivity analysis illustrates that fine content(FC),specific gravity(SG),and liquid limit(LL)highly influence the prediction of compaction parameters.

Toward Fine-grained Image Retrieval with Adaptive Deep Learning for Cultural Heritage Image

Fine-grained image classification is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a specific category caused by different poses and scales.A cul-tural heritage image is one of thefine-grained images because each image has the same similarity in most cases.Using the classification technique,distinguishing cultural heritage architecture may be difficult.This study proposes a cultural heri-tage content retrieval method using adaptive deep learning forfine-grained image retrieval.The key contribution of this research was the creation of a retrieval mod-el that could handle incremental streams of new categories while maintaining its past performance in old categories and not losing the old categorization of a cul-tural heritage image.The goal of the proposed method is to perform a retrieval task for classes.Incremental learning for new classes was conducted to reduce the re-training process.In this step,the original class is not necessary for re-train-ing which we call an adaptive deep learning technique.Cultural heritage in the case of Thai archaeological site architecture was retrieved through machine learn-ing and image processing.We analyze the experimental results of incremental learning forfine-grained images with images of Thai archaeological site architec-ture from world heritage provinces in Thailand,which have a similar architecture.Using afine-grained image retrieval technique for this group of cultural heritage images in a database can solve the problem of a high degree of similarity among categories and a high degree of dissimilarity for a specific category.The proposed method for retrieving the correct image from a database can deliver an average accuracy of 85 percent.Adaptive deep learning forfine-grained image retrieval was used to retrieve cultural heritage content,and it outperformed state-of-the-art methods infine-grained image retrieval.

Fine-Grained Multivariate Time Series Anomaly Detection in IoT

Sensors produce a large amount of multivariate time series data to record the states of Internet of Things(IoT)systems.Multivariate time series timestamp anomaly detection(TSAD)can identify timestamps of attacks and malfunctions.However,it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis,a process referred to as fine-grained anomaly detection(FGAD).Although further FGAD can be extended based on TSAD methods,existing works do not provide a quantitative evaluation,and the performance is unknown.Therefore,to tackle the FGAD problem,this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between indicators.Accordingly,this paper proposes a mul-tivariate time series fine-grained anomaly detection(MFGAD)framework.To avoid excessive fusion of features,MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained anomaly.Based on this framework,an algorithm based on Graph Attention Neural Network(GAT)and Attention Convolutional Long-Short Term Memory(A-ConvLSTM)is proposed,in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal indicators.Extensive simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.

Fine-Grained Action Recognition Based on Temporal Pyramid Excitation Network

Mining more discriminative temporal features to enrich temporal context representation is considered the key to fine-grained action recog-nition.Previous action recognition methods utilize a fixed spatiotemporal window to learn local video representation.However,these methods failed to capture complex motion patterns due to their limited receptive field.To solve the above problems,this paper proposes a lightweight Temporal Pyramid Excitation(TPE)module to capture the short,medium,and long-term temporal context.In this method,Temporal Pyramid(TP)module can effectively expand the temporal receptive field of the network by using the multi-temporal kernel decomposition without significantly increasing the computational cost.In addition,the Multi Excitation module can emphasize temporal importance to enhance the temporal feature representation learning.TPE can be integrated into ResNet50,and building a compact video learning framework-TPENet.Extensive validation experiments on several challenging benchmark(Something-Something V1,Something-Something V2,UCF-101,and HMDB51)datasets demonstrate that our method achieves a preferable balance between computation and accuracy.

Sedimentology and Paleoenvironmental Characteristics of Fine-grained Sediments in Coal-bearing Strata in the Eastern Ordos Basin:A Case Study of the Exploratory Well in the Zizhou Area

The continuously collected cores from the Permo-Carboniferous coal-bearing strata of the eastern Ordos Basin are essential for studying the hydrocarbon potential in this region.This study adopted sedimentological and geochemical methods to analyze the sedimentary environment,material composition,and geochemical characteristics of the coal-bearing strata.The differences in depositional and paleoclimatic conditions were compared;and the factors influencing the organic matter content of fine-grained sediments were explored.The depositional environment of the Benxi and Jinci formations was lagoon to tidal flat with weakly reduced waters with low salinity and dry-hot paleoclimatic conditions;while that of the Taiyuan Formation was a carbonate platform and shallow water delta front,where the water was highly reductive.The xerothermic climate alternated with the warm and humid climate.The period of maximum transgression in the Permo-Carboniferous has the highest water salinity.The Shanxi Formation was deposited in a shallow water delta front with a brackish and fresh water environment and alternative weak reductiveness.And the paleoclimate condition is dry-hot.The TOC content in fine-grained samples was averaging 1.52%.The main controlling mechanism of organic matter in this area was the input conditions according to the analysis on input and preservation of organic matter.

Fatigue crack propagation in fine-grained magnesium under low temperature tension-tension cyclic loading

Fine-grained magnesium was tested under stress-controlled tension-tension cyclic loading at -30 ℃ and the tested sample was observed using scanning electron microscope and electron backscatter diffraction to explore the fatigue behavior and crack propagation. The fatigue data showed that the material experienced cyclic softening followed by cyclic hardening before the final fracture failure. The microscopic observations demonstrated that the cracks were almost perpendicular to the loading direction with some zigzags and the cracks progressed along both small angle grain boundaries and large angle grain boundaries. Although the cracks were mainly propagated along large angle grain boundaries, the value of grain boundary angle was not the primary factor to determine the crack propagation direction. The local residual strain from the rolling process was released due to the crack propagation and there was more strain relaxation at regions closer to the cracks.

Fine-Grained Classification of Remote Sensing Ship Images Based on Improved VAN

The remote sensing ships’fine-grained classification technology makes it possible to identify certain ship types in remote sensing images,and it has broad application prospects in civil and military fields.However,the current model does not examine the properties of ship targets in remote sensing images with mixed multi-granularity features and a complicated backdrop.There is still an opportunity for future enhancement of the classification impact.To solve the challenges brought by the above characteristics,this paper proposes a Metaformer and Residual fusion network based on Visual Attention Network(VAN-MR)for fine-grained classification tasks.For the complex background of remote sensing images,the VAN-MR model adopts the parallel structure of large kernel attention and spatial attention to enhance the model’s feature extraction ability of interest targets and improve the classification performance of remote sensing ship targets.For the problem of multi-grained feature mixing in remote sensing images,the VAN-MR model uses a Metaformer structure and a parallel network of residual modules to extract ship features.The parallel network has different depths,considering both high-level and lowlevel semantic information.The model achieves better classification performance in remote sensing ship images with multi-granularity mixing.Finally,the model achieves 88.73%and 94.56%accuracy on the public fine-grained ship collection-23(FGSC-23)and FGSCR-42 datasets,respectively,while the parameter size is only 53.47 M,the floating point operations is 9.9 G.The experimental results show that the classification effect of VAN-MR is superior to that of traditional CNNs model and visual model with Transformer structure under the same parameter quantity.

Fine-Grained Features for Image Captioning

Image captioning involves two different major modalities(image and sentence)that convert a given image into a language that adheres to visual semantics.Almost all methods first extract image features to reduce the difficulty of visual semantic embedding and then use the caption model to generate fluent sentences.The Convolutional Neural Network(CNN)is often used to extract image features in image captioning,and the use of object detection networks to extract region features has achieved great success.However,the region features retrieved by this method are object-level and do not pay attention to fine-grained details because of the detection model’s limitation.We offer an approach to address this issue that more properly generates captions by fusing fine-grained features and region features.First,we extract fine-grained features using a panoramic segmentation algorithm.Second,we suggest two fusion methods and contrast their fusion outcomes.An X-linear Attention Network(X-LAN)serves as the foundation for both fusion methods.According to experimental findings on the COCO dataset,the two-branch fusion approach is superior.It is important to note that on the COCO Karpathy test split,CIDEr is increased up to 134.3%in comparison to the baseline,highlighting the potency and viability of our method.

On fine-grained visual explanation in convolutional neural networks

Existing explanation methods for Convolutional Neural Networks(CNNs)lack the pixel-level visualization explanations to generate the reliable fine-grained decision features.Since there are inconsistencies between the explanation and the actual behavior of the model to be interpreted,we propose a Fine-Grained Visual Explanation for CNN,namely F-GVE,which produces a fine-grained explanation with higher consistency to the decision of the original model.The exact backward class-specific gradients with respect to the input image is obtained to highlight the object-related pixels the model used to make prediction.In addition,for better visualization and less noise,F-GVE selects an appropriate threshold to filter the gradient during the calculation and the explanation map is obtained by element-wise multiplying the gradient and the input image to show fine-grained classification decision features.Experimental results demonstrate that F-GVE has good visual performances and highlights the importance of fine-grained decision features.Moreover,the faithfulness of the explanation in this paper is high and it is effective and practical on troubleshooting and debugging detection.

The control of astronomical cycles on lacustrine fine-grained event Sedimentation——A case study of the Chunshang sub-member of the upper Es_(4) in the Dongying Sag

Fine-grained lacustrine sedimentation controlled by astronomical cycles remains a research weakness in sedimentology studies,as most studies have concentrated on how astronomical cycles affect the normal lacustrine fine-grained sedimentation,but how they affect the lacustrine fine-grained event sedimen-tation has been rarely studied.Therefore,this work researched the characteristics of event sedimentation by systematically observing the cores from 30 cored wells in the Shahejie Formation of the Dongying Sag at a depth of over 1800 m,with more than 4000 thin sections being authenticated and over 1000 whole rocks being analyzed by X-ray diffraction(XRD).The research object was the Chunshang Sub-member of Upper Es_(4) in the Fanye 1 well,as it had the most comprehensive analysis data and underwent the most continuous coring.We divided astronomical cycles into different orders and made corresponding curves using the gamma-ray(GR)curve,spectral analysis,power spectrum estimation,and module extreme values,there were 6 long eccentricity periods,22 short eccentricity periods,65.5 obliquity cycles,and 110.5 precession cycles in this sub-member.On this basis,this study analyzed the control of astronomical cycles on the lacustrine fine-grained event sedimentation,and the research shows deposits were developed by slide-slump,turbidities,hyperpycnites,and tempestites in the Chunshang Sub-member of the Upper Es_(4),with higher long eccentricity,the monsoon climate contributes to the formation of storm currents,while with lower long eccentricity,the surface deposits are severely eroded by rivers and rainfalls,thus developing the slide-slump,turbidities,and hyperpycnites.The relationship between the lacustrine fine-grained event sedimentation and astronomical cycles was studied in this case study,which can promote research on fine-grained sedimentary rocks in genetic dynamics and boost the theoretical and disciplinary development in fine-grained sedimentology.

Multi-Branch Deepfake Detection Algorithm Based on Fine-Grained Features

With the rapid development of deepfake technology,the authenticity of various types of fake synthetic content is increasing rapidly,which brings potential security threats to people’s daily life and social stability.Currently,most algorithms define deepfake detection as a binary classification problem,i.e.,global features are first extracted using a backbone network and then fed into a binary classifier to discriminate true or false.However,the differences between real and fake samples are often subtle and local,and such global feature-based detection algorithms are not optimal in efficiency and accuracy.To this end,to enhance the extraction of forgery details in deep forgery samples,we propose a multi-branch deepfake detection algorithm based on fine-grained features from the perspective of fine-grained classification.First,to address the critical problem in locating discriminative feature regions in fine-grained classification tasks,we investigate a method for locating multiple different discriminative regions and design a lightweight feature localization module to obtain crucial feature representations by augmenting the most significant parts of the feature map.Second,using information complementation,we introduce a correlation-guided fusion module to enhance the discriminative feature information of different branches.Finally,we use the global attention module in the multi-branch model to improve the cross-dimensional interaction of spatial domain and channel domain information and increase the weights of crucial feature regions and feature channels.We conduct sufficient ablation experiments and comparative experiments.The experimental results show that the algorithm outperforms the detection accuracy and effectiveness on the FaceForensics++and Celeb-DF-v2 datasets compared with the representative detection algorithms in recent years,which can achieve better detection results.

Genesis of granular calcite in lacustrine fine-grained sedimentary rocks and its indication to volcanichydrothermal events: A case study of Permian Lucaogou Formation in Jimusar Sag, Junggar Basin, NW China

Granular calcite is an authigenic mineral in fine-grained sedimentary rocks.Core observation,thin section observation,cathodoluminescence analysis,fluid inclusion analysis,scanning electron microscope(SEM),and isotopic composition analysis were combined to clarify the genesis of granular calcite in the lacustrine fine-grained sedimentary rocks of the Permian Lucaogou Formation in the Jimusar Sag,Junggar Basin.It is found that the granular calcite is distributed with laminated characteristics in fine-grained sedimentary rocks in tuffite zones(or the transitional zone between tuffite and micritic dolomite).Granular calcite has obvious cathodoluminesence band,and it can be divided into three stages.Stage-Ⅰ calcite,with non-luminesence,high content of Sr element,inclusions containing Cos,and homogenization temperature higher than 170℃,was directly formed from the volcanic-hydrothermal deposition.Stage-Ⅱ calcite,with bright yellow luminescence,high contents of Fe,Mn and Mg,enrichment of light rare earth elements(LREEs),and high homogenization temperature,was formed by recrystallization of calcareous edges from exhalative hydrothermal deposition.Stage-IlI calcite,with dark orange luminescence band,high contents of Mg,P,V and other elements,no obvious fractionation among LREEs,and low homogenization temperature,was originated from diagenetic transformation during burial.The granular calcite appears regularly in the vertical direction and its formation temperature decreases from the center to the margin of particles,providing direct evidences for volcanic-hydrothermal events during the deposition of the Lucaogou Formation.The volcanic-hydrothermal event was conducive to the enrichment of organic matters in fine-grained sedimentary rocks of the Lucaogrou Formation,and positive to the development of high-quality source rocks.The volcanic-hydrothermal sediments might generate intergranular pores/fractures during the evolution,creating conditions for the self-generation and self-storage of shale oil.

Cycles of fine-grained sedimentation and their influences on organic matter distribution in the second member of Paleogene Kongdian Formation in Cangdong Sag,Bohai Bay Basin,East China

According to the theory of sequence stratigraphy based on continental transgressive-regressive(T-R)cycles,a 500 m continuous core taken from the second member of Kongdian Formation(Kong 2 Member)of Paleogene in Well G108-8 in the Cangdong Sag,Bohai Bay Basin,was tested and analyzed to clarify the high-frequency cycles of deep-water fine-grained sedimentary rocks in lacustrine basins.A logging vectorgraph in red pattern was plotted,and then a sequence stratigraphic framework with five-order high-frequency cycles was formed for the fine-grained sedimentary rocks in the Kong 2 Member.The high-frequency cycles of fine-grained sedimentary rocks were characterized by using different methods and at different scales.It is found that the fifth-order T cycles record a high content of terrigenous clastic minerals,a low paleosalinity,a relatively humid paleoclimate and a high density of laminae,while the fifth-order R cycles display a high content of carbonate minerals,a high paleosalinity,a dry paleoclimate and a low density of laminae.The changes in high-frequency cycles controlled the abundance and type of organic matter.The T cycles exhibit relatively high TOC and abundant endogenous organic matters in water in addition to terrigenous organic matters,implying a high primary productivity of lake for the generation and enrichment of shale oil.